Atmospheric Temperature vs Niño#
https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf
import warnings
warnings.filterwarnings("ignore")
import os
import sys
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import df2img
sys.path.append("../../../../indicators_setup")
from ind_setup.colors import get_df_col, plotting_style
from ind_setup.tables import plot_df_table
plotting_style()
from ind_setup.core import fontsize
sys.path.append("../../../functions")
from data_downloaders import GHCN, download_oni_index
Define location and variables of interest#
country = 'Palau'
vars_interest = ['tmax', 'TMAX']
Get Data#
df_country = GHCN.get_country_code(country)
print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')
The GHCN code for Palau is PS
df_stations = GHCN.download_stations_info()
df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
print(f'There are {df_country_stations.shape[0]} stations in {country}')
There are 13 stations in Palau
GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
Using Koror Station#
Analysis of how much the maximum and minimum temperatures over time are changing.
The analysis of the difference between these 2 variables will allow us to know how the daily variability is being modified
id = 'PSW00040309' # Koror Station
dict_min = GHCN.extract_dict_data_var(GHCND_dir, 'TMIN', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
dict_max = GHCN.extract_dict_data_var(GHCND_dir, 'TMAX', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
st_data = pd.concat([dict_min['data'], (dict_max['data'])], axis=1).dropna()
st_data['TMIN'] = np.where(st_data['TMIN'] >50, np.nan, st_data['TMIN'])
ONI index#
https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)
import plotly.graph_objects as go
import pandas as pd
# Assume df1 and get_df_col() are predefined
lims = [-.5, .5]
# Create the base figure
fig = go.Figure()
# Add the main line plot
fig.add_trace(go.Scatter(
x=df1.index,
y=df1['ONI'],
mode='lines',
line=dict(color=get_df_col()[0], width=2),
name='ONI'
))
# Add horizontal lines (Thresholds)
hline_colors = ['grey', get_df_col()[1], 'grey']
hline_labels = ['Threshold', 'Zero Line', 'Threshold']
for i, y in enumerate([lims[0], 0, lims[1]]):
fig.add_trace(go.Scatter(
x=[df1.index[0], df1.index[-1]],
y=[y, y],
mode='lines',
line=dict(color=hline_colors[i], dash='dash'),
name=hline_labels[i],
showlegend=(i == 0) # Only show legend once
))
# Add fill for values greater than lims[1]
fig.add_trace(go.Scatter(
x=list(df1.index) + list(df1.index[::-1]),
y=list(df1['ONI'].where(df1['ONI'] > lims[1], lims[1])) + [lims[1]] * len(df1),
fill='toself',
mode='none',
fillcolor=get_df_col()[2],
opacity=0.7,
name='Above Threshold'
))
# Add fill for values less than lims[0]
fig.add_trace(go.Scatter(
x=list(df1.index) + list(df1.index[::-1]),
y=[lims[0]] * len(df1) + list(df1['ONI'].where(df1['ONI'] < lims[0], lims[0]))[::-1],
fill='toself',
mode='none',
fillcolor=get_df_col()[3],
opacity=0.7,
name='Below Threshold'
))
# Update layout
fig.update_layout(
title='ONI Index Plot',
xaxis_title='Time',
yaxis_title='ONI Index',
font=dict(size=fontsize),
legend=dict(orientation='h', y=-0.2),
plot_bgcolor='white',
margin=dict(l=50, r=50, t=50, b=50),
)
# Show the plot
fig.show()
fig, ax = plt.subplots(figsize = [15, 6])
df1.plot(ax = ax, color = get_df_col()[0], lw = 2)
ax.hlines([lims[0], 0, lims[1]], df1.index[0], df1.index[-1],
color = ['grey', get_df_col()[1], 'grey', get_df_col()[1]],
linestyle = '--', label = 'Thresholds')
ax.fill_between(df1.index, lims[1], df1.ONI, where = (df1.ONI > lims[1]), color = get_df_col()[2], alpha = 0.7)
ax.fill_between(df1.index, df1.ONI, lims[0], where = (df1.ONI < lims[0]), color = get_df_col()[3], alpha = 0.7)
ax.legend(fontsize = fontsize, ncol = 2)
ax.set_ylabel('ONI Index', fontsize = fontsize)
Text(0, 0.5, 'ONI Index')
st_data_monthly = st_data.resample('M').mean()
st_data_monthly.index = pd.DatetimeIndex(st_data_monthly.index).to_period('M').to_timestamp() + pd.offsets.MonthBegin(1)
st_data_monthly
| TMIN | TMAX | |
|---|---|---|
| DATE | ||
| 1951-08-01 | 23.967742 | 31.045161 |
| 1951-09-01 | 23.993548 | 30.345161 |
| 1951-10-01 | 23.990000 | 30.673333 |
| 1951-11-01 | 24.509677 | 31.425806 |
| 1951-12-01 | 24.360000 | 31.023333 |
| ... | ... | ... |
| 2024-09-01 | 25.900000 | 30.477419 |
| 2024-10-01 | 25.846667 | 29.926667 |
| 2024-11-01 | 25.855172 | 30.382759 |
| 2024-12-01 | 25.780952 | 30.185714 |
| 2025-01-01 | 25.925000 | 30.787500 |
882 rows × 2 columns
rolling_mean = 6 #months
df1['tmin'] = st_data_monthly['TMIN'].rolling(window=rolling_mean).mean()
df1['tmax'] = st_data_monthly['TMAX'].rolling(window=rolling_mean).mean()
df1['tdiff'] = df1['tmax'] - df1['tmin']
df1['tmean'] = (df1['tmax'] + df1['tmin'])/2
fig, ax = plt.subplots(figsize = [15, 6])
df1.ONI.plot(ax = ax, color = get_df_col()[0], lw = 2)
ax2 = ax.twinx()
df1.tmin.plot(ax = ax2, color = get_df_col()[1], lw = 2)
# df1.tmax.plot(ax = ax2, color = get_df_col()[1], lw = 2)
# df1.tdiff.plot(ax = ax2, color = get_df_col()[1], lw = 2)
# df1.tmean.plot(ax = ax2, color = get_df_col()[1], lw = 2)
<Axes: >
low_lim = np.nanmin(df1.tmin)
fig, ax = plt.subplots(figsize = [15, 6])
df1.tmin.plot(ax = ax, color = get_df_col()[1], lw = 2)
ax.fill_between(df1.index, low_lim, df1.tmin, where = (df1.ONI > lims[1]), color = get_df_col()[2],
alpha = 0.7, label = f'ONI over th: {lims[1]}')
ax.fill_between(df1.index, low_lim, df1.tmin, where = (df1.ONI < lims[0]), color = get_df_col()[3],
alpha = 0.7, label = f'ONI below th: {lims[0]}')
ax.fill_between(df1.index, low_lim, df1.tmin, where = ((df1.ONI > lims[0]) & (df1.ONI < lims[1])),
color = get_df_col()[6], alpha = 0.075)
ax.legend(fontsize=fontsize)
ax.set_title('TMIN and ONI', fontsize = fontsize)
ax.set_ylabel('TMIN [°C]', fontsize = fontsize)
ax.set_xlabel('Time', fontsize = fontsize)
Text(0.5, 0, 'Time')
low_lim = np.nanmin(df1.tmax)
fig, ax = plt.subplots(figsize = [15, 6])
df1.tmax.plot(ax = ax, color = get_df_col()[1], lw = 2)
ax.fill_between(df1.index, low_lim, df1.tmax, where = (df1.ONI > lims[1]), color = get_df_col()[2],
alpha = 0.7, label = f'ONI over th: {lims[1]}')
ax.fill_between(df1.index, low_lim, df1.tmax, where = (df1.ONI < lims[0]), color = get_df_col()[3],
alpha = 0.7, label = f'ONI below th: {lims[0]}')
ax.fill_between(df1.index, low_lim, df1.tmax, where = ((df1.ONI > lims[0]) & (df1.ONI < lims[1])),
color = get_df_col()[6], alpha = 0.075)
ax.legend(fontsize = fontsize)
ax.set_title('TMAX and ONI', fontsize = fontsize)
ax.set_ylabel('TMAX [°C]', fontsize = fontsize)
ax.set_xlabel('Time', fontsize = fontsize)
Text(0.5, 0, 'Time')
plt.figure(figsize=(5, 4))
sns.heatmap(df1.corr(), annot=True, cmap='coolwarm', vmin=-1, vmax=1)
plt.title('Correlation Heatmap')
plt.show()
df_format = np.round(df1.describe(), 2)
fig = plot_df_table(df_format)
df2img.save_dataframe(fig=fig, filename="getting_started.png")